One or more computer processors create a user-event localization model for an identified remote audience member in a plurality of identified remote audience members for an event. The one or more computer processors generate a virtual audience member based the identified remote audience member utilizing a trained generated adversarial network and one or more user preferences. The one or more computer processors present the generated virtual audience member in a location associated with the event. The one or more computer processors dynamically adjust a presented virtual audience member responsive to one or more event occurrences utilizing the created user-event localization model.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A computer-implemented method comprising: creating, by one or more computer processors, a user-event localization model for an identified remote audience member in a plurality of identified remote audience members for an event; generating, by one or more computer processors, a virtual audience member based the identified remote audience member utilizing a trained generated adversarial network and one or more user preferences; presenting, by one or more computer processors, the generated virtual audience member in a location associated with the event; and dynamically adjusting, by one or computer processors, a presented virtual audience member responsive to one or more event occurrences utilizing the created user-event localization model.
2. The computer-implemented method of claim 1 , wherein dynamically adjusting the presented virtual audience member responsive to the one or more event occurrences utilizing the created user-event localization model, comprises: predicting, by one or more computer processors, a user reaction and amplitude utilizing the created user-event localization model based on a real time event occurrence.
3. The computer-implemented method of claim 2 , further comprising: regenerating, by one or more computer processors, the virtual audience member utilizing the predicted user reaction; and presenting, by one or more computer processors, the regenerated virtual audience member.
4. The computer-implemented method of claim 3 , further comprising: monitoring, by one or computer processors, the user reaction to the real time event occurrence; and progressively regenerating, by one or more computer processors, the presented regenerated virtual audience member to match the user reaction.
5. The computer-implemented method of claim 4 , further comprising: retraining, by one or more computer processors, the created user-event location model based on the monitored user reaction.
6. The computer-implemented method of claim 1 , wherein the user-event localization model maps one or more event occurrences with one or more user reactions.
7. The computer-implemented method of claim 1 , further comprising: copying, by one or more computer processors, the presented virtual audience member to meet an audience member threshold.
8. The computer-implemented method of claim 1 , further comprising: presenting, by one or more computer processors, a generated virtual audience in a location associated with the identified remote audience member.
9. A computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the stored program instructions comprising: program instructions to create a user-event localization model for an identified remote audience member in a plurality of identified remote audience members for an event; program instructions to generate a virtual audience member based the identified remote audience member utilizing a trained generated adversarial network and one or more user preferences; program instructions to present the generated virtual audience member in a location associated with the event; and program instructions to dynamically adjust a presented virtual audience member responsive to one or more event occurrences utilizing the created user-event localization model.
10. The computer program product of claim 9 , wherein the program instructions, to dynamically adjust the presented virtual audience member responsive to the one or more event occurrences utilizing the created user-event localization model, comprise: program instructions to predict a user reaction and amplitude utilizing the created user-event localization model based on a real time event occurrence.
11. The computer program product of claim 10 , wherein the program instructions, stored on the one or more computer readable storage media, further comprise: program instructions to regenerate the virtual audience member utilizing the predicted user reaction; and program instructions to present the regenerated virtual audience member.
12. The computer program product of claim 11 , wherein the program instructions, stored on the one or more computer readable storage media, further comprise: program instructions to monitor the user reaction to the real time event occurrence; and program instructions to progressively regenerate presented regenerated virtual audience member to match the user reaction.
13. The computer program product of claim 12 , wherein the program instructions, stored on the one or more computer readable storage media, further comprise: program instructions to retrain the created user-event location model based on the monitored user reaction.
14. The computer program product of claim 9 , wherein the user-event localization model maps one or more event occurrences with one or more user reactions.
15. A computer system comprising: one or more computer processors; one or more computer readable storage media; and program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the stored program instructions comprising: program instructions to create a user-event localization model for an identified remote audience member in a plurality of identified remote audience members for an event; program instructions to generate a virtual audience member based the identified remote audience member utilizing a trained generated adversarial network and one or more user preferences; program instructions to present the generated virtual audience member in a location associated with the event; and program instructions to dynamically adjust a presented virtual audience member responsive to one or more event occurrences utilizing the created user-event localization model.
16. The computer system of claim 15 , wherein the program instructions, to dynamically adjust the presented virtual audience member responsive to the one or more event occurrences utilizing the created user-event localization model, comprise: program instructions to predict a user reaction and amplitude utilizing the created user-event localization model based on a real time event occurrence.
17. The computer system of claim 16 , wherein the program instructions, stored on the one or more computer readable storage media, further comprise: program instructions to regenerate the virtual audience member utilizing the predicted user reaction; and program instructions to present the regenerated virtual audience member.
18. The computer system of claim 17 , wherein the program instructions, stored on the one or more computer readable storage media, further comprise: program instructions to monitor the user reaction to the real time event occurrence; and program instructions to progressively regenerate presented regenerated virtual audience member to match the user reaction.
19. The computer system of claim 18 , wherein the program instructions, stored on the one or more computer readable storage media, further comprise: program instructions to retrain the created user-event location model based on the monitored user reaction.
20. The computer system of claim 15 , wherein the user-event localization model maps one or more event occurrences with one or more user reactions.
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November 5, 2020
May 24, 2022
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